Gaussian processes (GP) are considered as the gold standard for UE [1]}, [2]}.
Similarly, Bayesian Neural Networks (BNNs) yield mathematically grounded UE methods that extends standard neural network (NN) training to posterior inference.
However GPs and BNNs do not scale well with the dimension of the input data and the parameters space, respectively, and are often infeasible for real-world problems.
Thus, one of the most popular methods in deep learning is Monte Carlo Dropout (MCDropout), which method applies Dropout [3]} at inference time to compute uncertainty estimates.
An active branch of work is focused on finding better and more efficient approximation methods for UE.
In order for the community to move forward in the right direction, it is important to verify that the these UE methods work well and to understand their differences and flaws.
